Systems and methods have been developed for defining an object in video and for tracking that object through the frames of the video. In various applications, a person may be the “object” to be tracked. For example, sports images and applications using surveillance cameras are interested in following the actions of a person.
Previously related work mostly applies the background information to realize a discrimination measure. For example, some related work searches for the best scale in the scale space by Difference of Gaussian filters or level set functions that are time consuming. A simple method looks for the scale by searching based on the same metric in location estimation which results in the shrinkage problem. Some other related work uses multiple kernels to model the relationship between the target appearance and its motion characteristics that yields complex and noise-sensitive algorithms. Some related work addresses template update only and uses the Kalman filtering or adaptive alpha-blending to update the histogram, but still results in accumulation errors.